Zobrazeno 1 - 10
of 1 430
pro vyhledávání: '"Sabatelli P."'
Model-Free Reinforcement Learning (RL) algorithms either learn how to map states to expected rewards or search for policies that can maximize a certain performance function. Model-Based algorithms instead, aim to learn an approximation of the underly
Externí odkaz:
http://arxiv.org/abs/2411.11457
Lifelike visualizations in design, cinematography, and gaming rely on precise physics simulations, typically requiring extensive computational resources and detailed physical input. This paper presents a method that can infer a system's physical prop
Externí odkaz:
http://arxiv.org/abs/2409.15344
Production scheduling is an essential task in manufacturing, with Reinforcement Learning (RL) emerging as a key solution. In a previous work, RL was utilized to solve an extended permutation flow shop scheduling problem (PFSSP) for a real-world produ
Externí odkaz:
http://arxiv.org/abs/2406.02294
A recent method for solving zero-sum partially observable stochastic games (zs-POSGs) embeds the original game into a new one called the occupancy Markov game. This reformulation allows applying Bellman's principle of optimality to solve zs-POSGs. Ho
Externí odkaz:
http://arxiv.org/abs/2406.00054
Despite the considerable attention given to the questions of \textit{how much} and \textit{how to} explore in deep reinforcement learning, the investigation into \textit{when} to explore remains relatively less researched. While more sophisticated ex
Externí odkaz:
http://arxiv.org/abs/2403.17542
Recommendation systems, for documents, have become tools to find relevant content on the Web. However, these systems have limitations when it comes to recommending documents in languages different from the query language, which means they might overl
Externí odkaz:
http://arxiv.org/abs/2401.06583
Autor:
Müller, Arthur, Sabatelli, Matthia
Reinforcement Learning (RL) has been widely explored in Traffic Signal Control (TSC) applications, however, still no such system has been deployed in practice. A key barrier to progress in this area is the reality gap, the discrepancy that results fr
Externí odkaz:
http://arxiv.org/abs/2307.11357
In this paper we propose a novel bipedal locomotion controller that uses noisy exteroception to traverse a wide variety of terrains. Building on the cutting-edge advancements in attention based belief encoding for quadrupedal locomotion, our work ext
Externí odkaz:
http://arxiv.org/abs/2304.07236
Autor:
Wagenbach, Julius, Sabatelli, Matthia
We study whether the learning rate $\alpha$, the discount factor $\gamma$ and the reward signal $r$ have an influence on the overestimation bias of the Q-Learning algorithm. Our preliminary results in environments which are stochastic and that requir
Externí odkaz:
http://arxiv.org/abs/2210.05262
Autor:
Gaia Di Timoteo, Andrea Giuliani, Adriano Setti, Martina C. Biagi, Michela Lisi, Tiziana Santini, Alessia Grandioso, Davide Mariani, Francesco Castagnetti, Eleonora Perego, Sabrina Zappone, Serena Lattante, Mario Sabatelli, Dante Rotili, Giuseppe Vicidomini, Irene Bozzoni
Publikováno v:
Nature Communications, Vol 15, Iss 1, Pp 1-13 (2024)
Abstract Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disease due to gradual motoneurons (MN) degeneration. Among the processes associated to ALS pathogenesis, there is the formation of cytoplasmic inclusions produced by agg
Externí odkaz:
https://doaj.org/article/fba55743618c4cde8526ef8ffa63acac
